{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:22:40.858142Z",
     "start_time": "2017-12-23T09:22:38.889932Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据 & 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:22:44.705119Z",
     "start_time": "2017-12-23T09:22:41.238027Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>listing_id</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>price</th>\n",
       "      <th>price_bathrooms</th>\n",
       "      <th>price_bedrooms</th>\n",
       "      <th>room_diff</th>\n",
       "      <th>room_num</th>\n",
       "      <th>...</th>\n",
       "      <th>virtual</th>\n",
       "      <th>walk</th>\n",
       "      <th>walls</th>\n",
       "      <th>war</th>\n",
       "      <th>washer</th>\n",
       "      <th>water</th>\n",
       "      <th>wheelchair</th>\n",
       "      <th>wifi</th>\n",
       "      <th>windows</th>\n",
       "      <th>work</th>\n",
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       "  </thead>\n",
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       "      <td>1879.000000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7143442</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>40.7109</td>\n",
       "      <td>-73.9571</td>\n",
       "      <td>3300</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6860601</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>40.7650</td>\n",
       "      <td>-73.9845</td>\n",
       "      <td>4900</td>\n",
       "      <td>1633.333333</td>\n",
       "      <td>1633.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 225 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id  bathrooms  bedrooms  latitude  longitude  price  \\\n",
       "0     7142618        1.0         1   40.7185   -73.9865   2950   \n",
       "1     7210040        1.0         2   40.7278   -74.0000   2850   \n",
       "2     7103890        1.0         1   40.7306   -73.9890   3758   \n",
       "3     7143442        1.0         2   40.7109   -73.9571   3300   \n",
       "4     6860601        2.0         2   40.7650   -73.9845   4900   \n",
       "\n",
       "   price_bathrooms  price_bedrooms  room_diff  room_num  ...   virtual  walk  \\\n",
       "0      1475.000000     1475.000000        0.0       2.0  ...         0     0   \n",
       "1      1425.000000      950.000000       -1.0       3.0  ...         0     0   \n",
       "2      1879.000000     1879.000000        0.0       2.0  ...         0     0   \n",
       "3      1650.000000     1100.000000       -1.0       3.0  ...         0     0   \n",
       "4      1633.333333     1633.333333        0.0       4.0  ...         0     0   \n",
       "\n",
       "   walls  war  washer  water  wheelchair  wifi  windows  work  \n",
       "0      0    0       0      0           0     0        0     0  \n",
       "1      0    1       0      0           0     0        0     0  \n",
       "2      0    0       0      0           0     0        0     0  \n",
       "3      0    0       0      0           1     0        0     0  \n",
       "4      0    1       0      0           0     0        0     0  \n",
       "\n",
       "[5 rows x 225 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = pd.read_csv('RentListingInquries_FE_test.csv')\n",
    "target.head()\n",
    "#测试数据集前5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:22:48.401015Z",
     "start_time": "2017-12-23T09:22:46.309380Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>price</th>\n",
       "      <th>price_bathrooms</th>\n",
       "      <th>price_bedrooms</th>\n",
       "      <th>room_diff</th>\n",
       "      <th>room_num</th>\n",
       "      <th>Year</th>\n",
       "      <th>...</th>\n",
       "      <th>walk</th>\n",
       "      <th>walls</th>\n",
       "      <th>war</th>\n",
       "      <th>washer</th>\n",
       "      <th>water</th>\n",
       "      <th>wheelchair</th>\n",
       "      <th>wifi</th>\n",
       "      <th>windows</th>\n",
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       "      <th>interest_level</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>40.7539</td>\n",
       "      <td>-73.9677</td>\n",
       "      <td>3275</td>\n",
       "      <td>1637.5</td>\n",
       "      <td>1637.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>40.8241</td>\n",
       "      <td>-73.9493</td>\n",
       "      <td>3350</td>\n",
       "      <td>1675.0</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>-3.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 225 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bathrooms  bedrooms  latitude  longitude  price  price_bathrooms  \\\n",
       "0        1.5         3   40.7145   -73.9425   3000           1200.0   \n",
       "1        1.0         2   40.7947   -73.9667   5465           2732.5   \n",
       "2        1.0         1   40.7388   -74.0018   2850           1425.0   \n",
       "3        1.0         1   40.7539   -73.9677   3275           1637.5   \n",
       "4        1.0         4   40.8241   -73.9493   3350           1675.0   \n",
       "\n",
       "   price_bedrooms  room_diff  room_num  Year       ...        walk  walls  \\\n",
       "0      750.000000       -1.5       4.5  2016       ...           0      0   \n",
       "1     1821.666667       -1.0       3.0  2016       ...           0      0   \n",
       "2     1425.000000        0.0       2.0  2016       ...           0      0   \n",
       "3     1637.500000        0.0       2.0  2016       ...           0      0   \n",
       "4      670.000000       -3.0       5.0  2016       ...           0      0   \n",
       "\n",
       "   war  washer  water  wheelchair  wifi  windows  work  interest_level  \n",
       "0    0       0      0           0     0        0     0               1  \n",
       "1    0       0      0           0     0        0     0               2  \n",
       "2    0       0      0           0     0        0     0               0  \n",
       "3    0       0      0           0     0        0     0               2  \n",
       "4    1       0      0           0     0        0     0               2  \n",
       "\n",
       "[5 rows x 225 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('RentListingInquries_FE_train.csv')\n",
    "data.head()\n",
    "#训练集前5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:22:51.893378Z",
     "start_time": "2017-12-23T09:22:51.884309Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 49352 entries, 0 to 49351\n",
      "Columns: 225 entries, bathrooms to interest_level\n",
      "dtypes: float64(7), int64(218)\n",
      "memory usage: 84.7 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:22:54.939781Z",
     "start_time": "2017-12-23T09:22:54.932129Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 74659 entries, 0 to 74658\n",
      "Columns: 225 entries, listing_id to work\n",
      "dtypes: float64(7), int64(218)\n",
      "memory usage: 128.2 MB\n"
     ]
    }
   ],
   "source": [
    "target.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-22T05:40:26.067969Z",
     "start_time": "2017-12-22T05:40:26.064464Z"
    },
    "collapsed": true
   },
   "source": [
    "### 分析用户感兴趣程度的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:22:57.710408Z",
     "start_time": "2017-12-23T09:22:57.696158Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    34284\n",
       "1    11229\n",
       "0     3839\n",
       "Name: interest_level, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interest_level_count = data['interest_level'].value_counts()\n",
    "# 'low' : 2, 'medium' : 1, 'high' : 0 \n",
    "interest_level_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:00.989117Z",
     "start_time": "2017-12-23T09:23:00.647040Z"
    },
    "run_control": {
     "marked": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OKpL6AT8HDgVGAsdIGtncUpmZbbxaOqgA+wLtEfFYRLwGzADGNblMZmYbrf7N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\nhXFQMTOzwjiomJlZYf4/p2l4SpjmUBsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1068600f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def barplot(x_data, y_data, x_label, y_label, title):\n",
    "    _, ax = plt.subplots()\n",
    "    ax.bar(x_data, y_data, color = '#539caf', align = 'center')\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_title(title)\n",
    "\n",
    "barplot(interest_level_count.index,\n",
    "        interest_level_count,\n",
    "        \"Interest'level\",\"Values\",\"The Interest'level from Rental Listing Inquiries\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 不感兴趣的比例比较大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析租赁价的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:03.871197Z",
     "start_time": "2017-12-23T09:23:03.668223Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:517: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "def remove_noise(df):\n",
    "#remove some noise\n",
    "    df= df[df.price < 10000]\n",
    "\n",
    "    df.loc[df[\"bathrooms\"] == 112, \"bathrooms\"] = 1.5\n",
    "    df.loc[df[\"bathrooms\"] == 10, \"bathrooms\"] = 1\n",
    "    df.loc[df[\"bathrooms\"] == 20, \"bathrooms\"] = 2\n",
    "    return df\n",
    "data = remove_noise(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:04.057748Z",
     "start_time": "2017-12-23T09:23:03.874874Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a10973550>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10973e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['price'],bins=30,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 价格分布呈左正态分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T03:52:55.320539Z",
     "start_time": "2017-12-23T03:52:55.315753Z"
    }
   },
   "source": [
    "### 分析bathroom的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:10.028685Z",
     "start_time": "2017-12-23T09:23:09.843252Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0e4604e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0e449c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['bathrooms'],bins=30,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 浴室为1的占绝大部分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析bedroom的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:13.050361Z",
     "start_time": "2017-12-23T09:23:12.785545Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0de0ff60>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0de0f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['bedrooms'],bins=30,kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-22T06:39:22.819011Z",
     "start_time": "2017-12-22T06:39:22.815926Z"
    }
   },
   "source": [
    "### 分析用户感兴趣程度和房价的的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:13.074641Z",
     "start_time": "2017-12-23T09:23:13.054091Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def boxplot( data , x_value , y_value , base_color , median_color ):\n",
    "    _, ax = plt.subplots()\n",
    "\n",
    "    x_feature = data[x_value].unique()\n",
    "    bp_data = []\n",
    "    for val in x_feature:\n",
    "        bp_data.append(data[data[x_value] == val][y_value].values)\n",
    "    \n",
    "    ax.boxplot(bp_data\n",
    "               , patch_artist = True\n",
    "               , medianprops = {'color': base_color}\n",
    "               , boxprops = {'color': base_color, 'facecolor': median_color}\n",
    "               , whiskerprops = {'color': median_color}\n",
    "               , capprops = {'color': base_color})\n",
    "\n",
    "    ax.set_xticklabels(x_feature)\n",
    "    ax.set_ylabel('Values of %s' % y_value)\n",
    "    ax.set_xlabel('Features of the %s' % x_value)     \n",
    "    ax.set_title('The box with %d features for the %s' % (len(x_feature),y_value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:18.618170Z",
     "start_time": "2017-12-23T09:23:18.389757Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GG2+wYMECampqfJRiZrlwUukHrrrqKqZOncrAgQOZOnUqV111Vd4hmVmF8sWP/cD111/f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10c1eb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot( data = data,\n",
    "         x_value = 'interest_level' ,\n",
    "         y_value = 'price',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 租赁平均价格越高，用户越不感兴趣"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析用户感兴趣程度和单位bathroom的价格的的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:21.800137Z",
     "start_time": "2017-12-23T09:23:21.551769Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NnFTMclZdXc25557Lhg0bANiwYQPnnnuuE4uVJScVs5xdcMEF7LLLLixYsIDa2loWLFjA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\n+f8Q8j8NS/w1IgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0e75e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot( data = data,\n",
    "         x_value = 'interest_level' ,\n",
    "         y_value = 'price_bathrooms',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析用户感兴趣程度和单位bedroom的价格的的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:24.812250Z",
     "start_time": "2017-12-23T09:23:24.553747Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CLf1HR8Q5JYnK2vXUU09xxRVXtOqe5KKLLuKMM84od2hm1s/kTR43poeV0YQJ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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1000d9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot( data = data,\n",
    "         x_value = 'interest_level' ,\n",
    "         y_value = 'price_bedrooms',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 房间的单价越高，用户越不感兴趣"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析用户感兴趣程度和单位bedroom的数的的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:27.715899Z",
     "start_time": "2017-12-23T09:23:27.462307Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iFgILAdra2hr2LS/DzjKSefs1Lm+7/Irs0nkI2KtifE/gkQLbMzOzKopM+DcB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0e3181d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot( data = data,\n",
    "         x_value = 'interest_level' ,\n",
    "         y_value = 'bedrooms',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-22T13:44:04.615362Z",
     "start_time": "2017-12-22T13:44:04.610838Z"
    }
   },
   "source": [
    "### 分析用户感兴趣程度和单位bathroom的数的的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:30.956126Z",
     "start_time": "2017-12-23T09:23:30.704705Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BJH1D0iZJ6xodiw2cpMMkrZTUJWm9pAsaHZPlJ+kMSXdL2iDpk42Op9Hchl8w\nSacATwJXRMS4RsdjAyPppcBLI+J2SfsCq4E3R8RdDQ7N+iGpHbgHmETWIdNtwPQyl51r+AWLiJsA\n91reoiLidxFxexp/AuiiSt/M1pT+HNgQEfdGxB+Aq4FpDY6poZzwzXKSNAY4Hri1sZFYTocCD1ZM\nP0TJP6yd8M1ykDQC+B7woYh4vNHxWC7VOiEudRu2E75ZPyQNI0v2V0bE9xsdj+X2EHBYxfTLgN82\nKJam4IRvVoOyXsyXAF0RcVGj47EBuQ14paQjJO0J/DVwbYNjaign/IJJWgr8HDhK0kOSZjQ6JhuQ\nk4F3AadJWpOGMxsdlPUvIp4HPgAsI7vYfk1ErG9sVI3l2zLNzErCNXwzs5JwwjczKwknfDOzknDC\nNzMrCSd8M7OScMI3MysJJ3wDQNL2ivvM16Tnxgx0HQdIOr/+0e0aSV9KjzT+Uq/5p0p6XcX05ZLe\nuhvbuV7SAf0sc56kQ3Z1GznjGCPp3H6WOVXSdXXebt3XacXYo9EBWNN4JiKO2811HACcD1wykDdJ\nao+I7bu57WreB4yKiOd6zT+V7JHVt9RjIxGR54dY5wHrGMBP+yXtkX48lNcY4FzgqgG8x0rENXzr\nk6T2VEu+TdKdkt6X5o+QdIOk2yWtldT9yNkvAkembwhf6l3zk/RVSeel8fslfUbSKuBtko6U9J+S\nVkv6L0l/mpZ7m6R1ku6QdFOVGJW2tS7F8vY0/1pgOHBr97w0fwwwC/hwivP16aVTJN0i6d7K2r6k\nj1fs/z/0cZzul3RQqmF3SbosfbNYLmmftL4O4Mq0zX0knSDpZ2l/l6Xn7iPpRklfkPQz4AJJoyR9\nL8Vwm6ST03JvqPg29sv0rP4vAq9P8z6co3yHK+ug57a0jmlp/q2SjqlY7sYUb9XlrYVEhAcPANuB\nNWn4QZo3E/h0Gt8L6ASOIPtmuF+afxCwgezJhGOAdRXrPBW4rmL6q8B5afx+4BMVr90AvDKNnwj8\nNI2vBQ5N4wdUifstwAqgHTgYeICswxKAJ/vY188BH6uYvhz4DlkF6GiyZ6gDTAYWp31rA64DTqmy\nvvvTcRgDPA8cl+ZfA7wzjd8IdKTxYWTfLkal6bcD36hY7pKKdV8FTEjjh5M90wfgh8DJaXxEKpMe\nx7uPfd+5DPCFivgOIOssZDjwYeAf0vyXAvf0s3y/2/XQHIObdKxbtSadycD4ihrv/sAryZ5C+AVl\nvXntIHvG+MG7sM1/h52PHn4d8B1p5xNt90p/bwYul3QNUO1JlROApZE1Cf0+1Yxfw8AfkvUfEbED\nuEtS975MTsMv0/QIsv1/0TfNPJC1AAACeElEQVSNCvdFxJo0vprsQ6C3o4BxwIq0v+3A7ype//eK\n8dOBoyuOy36pNn8zcJGkK4HvR8RDFcvkNRmYKuljaXpvsg+Va8g+RD8L/BXZh2Gt5a1FOOFbLQLm\nRMSyHjOzZplRwAkRsU3S/WT//L09T89mw97LPJX+tgFbq3zgEBGzJJ0InAWskXRcRGzpFWM9VLbz\nq+Lvgoi4dBfXsx3Yp8oyAtZHxEl9rOOpivE24KSIeKbXMl+U9CPgTOC/JZ0+gBgr43hLRNz9ohek\nLZLGk337eF+t5Ss+IK3JuQ3falkGzFb2PHgkvUrScLKa/qaU7CcCo9PyTwD7Vrx/I1ntdC9J+wNv\nrLaRyDoUuU/S29J2JOnVafzIiLg1Ij4DPELP55tDVtt+u7LrDaOAU4Bf9LNfveOstf9/m76BIOlQ\nSS/J8b7+tnk3MErSSWm9wyrbzHtZTvbER9Kyx6W/R0bE2oi4kKyp7U/Jv1/dlgFzlL4aSDq+4rWr\ngU8A+0fE2hzLWwtwwrda/hW4C7hd0jrgUrJvhVcCHZI6gXcAvwJINe+b0wXUL0XEg2TNA3em9/yy\nyja6vQOYIekOYD0v9D36pXQxdh1Zcr+j1/t+kNZ/B/BTsusCD/ezXz8E/qLXRdsXiYjlZG3oP5e0\nFvguA0uolS4Hvi5pDVkTzluBC9P+riFr0qrmg2TH+k5Jd5FdcAb4UDrOdwDPAD8mOw7PK7vA3e9F\nW+DzZNcT7kzH9/MVr32X7Pnx1+Rc3lqAH49sZlYSruGbmZWEL9qaDUGSpgAX9pp9X0T8RSPisebg\nJh0zs5Jwk46ZWUk44ZuZlYQTvplZSTjhm5mVxP8H4RBCPsvRGxUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10689c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot( data = data,\n",
    "         x_value = 'interest_level' ,\n",
    "         y_value = 'bathrooms',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析用户感兴趣程度和单位room_num的数的的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:34.195257Z",
     "start_time": "2017-12-23T09:23:33.916634Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YMWNGp7IZM2awbdu2giKyvsieEZSaN6sleauJrgW+C/wd8AHg/elfG4DGxkba2to6lbW1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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0fffbb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot( data = data,\n",
    "         x_value = 'interest_level' ,\n",
    "         y_value = 'room_num',\n",
    "         base_color = 'b',\n",
    "         median_color = 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 房间数为 2～ 4个用户感兴趣程度一般"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:23:37.248614Z",
     "start_time": "2017-12-23T09:23:37.192294Z"
    },
    "collapsed": true,
    "run_control": {
     "marked": true
    }
   },
   "outputs": [],
   "source": [
    "y_train = data['interest_level']\n",
    "X_train = data.drop('interest_level',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:24:04.075056Z",
     "start_time": "2017-12-23T09:24:03.836346Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 数据标准化 \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:24:07.238187Z",
     "start_time": "2017-12-23T09:24:07.182588Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, train_size = 0.8,random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用LogisticRegressionCV实现正则化的 Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### L1正则"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### saga优化求解器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:39:23.583418Z",
     "start_time": "2017-12-23T09:24:20.971991Z"
    },
    "run_control": {
     "marked": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/sag.py:326: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "           class_weight=None, cv=5, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1.0, max_iter=100, multi_class='ovr',\n",
       "           n_jobs=1, penalty='l1', random_state=None, refit=True,\n",
       "           scoring='neg_log_loss', solver='saga', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [0.001,0.01,0.1,1, 10,100,1000]\n",
    "\n",
    "lrcv_L1 = LogisticRegressionCV(Cs=Cs, cv = 5, scoring='neg_log_loss', penalty='l1', solver='saga', multi_class='ovr')\n",
    "lrcv_L1.fit(X_train, y_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:43:21.708543Z",
     "start_time": "2017-12-23T09:43:21.699878Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: array([[-0.27254105, -0.23219699, -0.23116278, -0.23408504, -0.2356508 ,\n",
       "         -0.2365802 , -0.23722574],\n",
       "        [-0.27249049, -0.23448124, -0.23325467, -0.23529115, -0.2366366 ,\n",
       "         -0.23758607, -0.23832002],\n",
       "        [-0.27217875, -0.233008  , -0.22835057, -0.22978444, -0.23108697,\n",
       "         -0.23197064, -0.23261359],\n",
       "        [-0.27205819, -0.23274466, -0.22950964, -0.23425878, -0.23678406,\n",
       "         -0.2383641 , -0.23953849],\n",
       "        [-0.27224139, -0.23421348, -0.23349869, -0.236951  , -0.23903415,\n",
       "         -0.24047729, -0.24158012]]),\n",
       " 1: array([[-0.52611115, -0.49621515, -0.49577684, -0.49913524, -0.50062338,\n",
       "         -0.50153191, -0.5022119 ],\n",
       "        [-0.52561982, -0.49469079, -0.49504763, -0.49921282, -0.50130956,\n",
       "         -0.50247787, -0.50322862],\n",
       "        [-0.5261723 , -0.49938948, -0.50089758, -0.50327106, -0.50411263,\n",
       "         -0.5046108 , -0.50500484],\n",
       "        [-0.52678259, -0.49851912, -0.49781955, -0.50020502, -0.50122859,\n",
       "         -0.50180586, -0.50221474],\n",
       "        [-0.52561069, -0.49791873, -0.50049992, -0.5047238 , -0.50709387,\n",
       "         -0.50854644, -0.50927374]]),\n",
       " 2: array([[-0.55849869, -0.51239526, -0.50836261, -0.51041603, -0.51125388,\n",
       "         -0.51170994, -0.51204108],\n",
       "        [-0.55928033, -0.51593585, -0.51237638, -0.51267412, -0.51296576,\n",
       "         -0.51315209, -0.51329606],\n",
       "        [-0.56123802, -0.52524512, -0.52789758, -0.53038971, -0.5315017 ,\n",
       "         -0.5322589 , -0.53287914],\n",
       "        [-0.56058748, -0.51913782, -0.51527918, -0.51611902, -0.51638837,\n",
       "         -0.51647614, -0.51654481],\n",
       "        [-0.55941752, -0.52317254, -0.52525745, -0.52876668, -0.53096136,\n",
       "         -0.53250906, -0.53370909]])}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L1.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:43:27.303320Z",
     "start_time": "2017-12-23T09:43:27.124767Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JUzz3yksKuXTZPLY0H+VP35nq1oiITE+yVyRlZrZueCEYDC8LFgfG3oVDQFXC8jLgSMJy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\n/wMjtVL+oj4LPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0f4ce8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_Cs = len(Cs)\n",
    "n_classes = 3\n",
    "scores =  np.zeros((n_classes,n_Cs))\n",
    "\n",
    "for j in range(n_classes):\n",
    "        scores[j][:] = np.mean(lrcv_L1.scores_[j],axis = 0)\n",
    "    \n",
    "mse_mean = -np.mean(scores, axis = 0)\n",
    "plt.plot(np.log10(Cs), mse_mean.reshape(n_Cs,1)) \n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('neg-logloss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T09:43:42.713831Z",
     "start_time": "2017-12-23T09:43:42.695354Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1.71853844e-01,   3.76910621e-02,  -9.88077093e-03,\n",
       "         -7.89695154e-02,  -3.22818454e-01,  -1.08996947e+00,\n",
       "         -3.63489714e-01,  -3.08546996e-01,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,  -1.00445583e-01,\n",
       "          4.52975749e-02,  -7.59500748e-03,   2.66512318e-01,\n",
       "         -1.21349959e-01,   1.47927571e-03,   1.41096682e-01,\n",
       "          7.16017711e-03,  -1.29349947e-01,  -1.15156486e-01,\n",
       "         -1.55685521e-03,   0.00000000e+00,  -6.89834274e-03,\n",
       "         -5.72958330e-02,   0.00000000e+00,  -7.20368277e-02,\n",
       "          0.00000000e+00,  -5.41015716e-02,   0.00000000e+00,\n",
       "          6.25770949e-02,  -1.00173434e-01,   0.00000000e+00,\n",
       "          0.00000000e+00,   2.34128087e-02,  -6.04551852e-02,\n",
       "         -2.64991378e-02,  -5.97237634e-02,  -3.44899887e-04,\n",
       "          2.50872682e-02,   4.92559445e-02,  -7.15958890e-02,\n",
       "          2.41691285e-02,  -1.31334956e-02,   0.00000000e+00,\n",
       "          1.54317837e-02,   0.00000000e+00,   0.00000000e+00,\n",
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       "          3.13651687e-03,   0.00000000e+00],\n",
       "       [  3.27546857e-01,   0.00000000e+00,   1.80184388e-01,\n",
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       "          0.00000000e+00,   0.00000000e+00,   3.39058384e-02,\n",
       "         -3.46567059e-02,   3.52754624e-02,  -2.82244422e-01,\n",
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       "          2.04059471e-02,   1.43632912e-01,  -5.81360446e-03,\n",
       "          1.73738252e-02,  -5.03292431e-03,  -5.44320791e-02,\n",
       "          1.52045253e-02,   1.41088785e-02,   2.16705787e-02,\n",
       "          4.16295392e-02,   4.81718385e-02,  -2.47191581e-04,\n",
       "         -3.50220183e-02,   3.94689890e-02,   0.00000000e+00,\n",
       "          0.00000000e+00,  -3.04439855e-02,   0.0000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<b>limit_output extension: Maximum message size of 10000 exceeded with 13899 characters</b>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrcv_L1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-24T01:45:51.605300Z",
     "start_time": "2017-12-24T01:45:51.581128Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.70723514211886307"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 当 C 的值为 0.1 lossloss 最小 ，此时的评分最高\n",
    "lrcv_L1.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### L2正则"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T10:49:49.478694Z",
     "start_time": "2017-12-23T10:49:49.474138Z"
    }
   },
   "source": [
    "##### lbfgs优化求解器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T10:08:06.728657Z",
     "start_time": "2017-12-23T10:07:01.290275Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "           class_weight=None, cv=5, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1.0, max_iter=100, multi_class='ovr',\n",
       "           n_jobs=1, penalty='l2', random_state=None, refit=True,\n",
       "           scoring='neg_log_loss', solver='lbfgs', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [0.001,0.01,0.1,1, 10,100,1000]\n",
    "\n",
    "lrcv_L2 = LogisticRegressionCV(Cs=Cs, cv = 5, scoring='neg_log_loss', penalty='l2', solver='lbfgs', multi_class='ovr')\n",
    "lrcv_L2.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T10:08:48.151802Z",
     "start_time": "2017-12-23T10:08:48.142423Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: array([[-0.23713281, -0.23306826, -0.23686684, -0.24290483, -0.24973698,\n",
       "         -0.25204039, -0.25503019],\n",
       "        [-0.23837071, -0.23435053, -0.23861019, -0.24598021, -0.25222149,\n",
       "         -0.25532887, -0.2553719 ],\n",
       "        [-0.23522708, -0.2290926 , -0.23287703, -0.23974038, -0.24588   ,\n",
       "         -0.24879483, -0.24887243],\n",
       "        [-0.23514174, -0.23205109, -0.239275  , -0.25044932, -0.26290734,\n",
       "         -0.2651288 , -0.26608821],\n",
       "        [-0.23795956, -0.23546952, -0.24163321, -0.24958364, -0.25871184,\n",
       "         -0.26380582, -0.26402692]]),\n",
       " 1: array([[-0.49674526, -0.49777835, -0.50185045, -0.50806156, -0.51234247,\n",
       "         -0.51364566, -0.51418876],\n",
       "        [-0.49486786, -0.49722318, -0.50230035, -0.50730216, -0.51156117,\n",
       "         -0.5119947 , -0.51253829],\n",
       "        [-0.50067559, -0.50232675, -0.50519385, -0.51207931, -0.51726715,\n",
       "         -0.51788116, -0.51799431],\n",
       "        [-0.49931742, -0.49952604, -0.50194352, -0.50605933, -0.50792071,\n",
       "         -0.51256916, -0.512436  ],\n",
       "        [-0.49914269, -0.50235203, -0.50888511, -0.51319209, -0.51758455,\n",
       "         -0.51698956, -0.51777067]]),\n",
       " 2: array([[-0.51508241, -0.51046482, -0.51126674, -0.51379844, -0.51839503,\n",
       "         -0.51942249, -0.51915073],\n",
       "        [-0.51698923, -0.51327941, -0.5131671 , -0.51489557, -0.51629932,\n",
       "         -0.51635781, -0.51689872],\n",
       "        [-0.52865699, -0.52903058, -0.53204094, -0.53849195, -0.54339503,\n",
       "         -0.54385575, -0.54484515],\n",
       "        [-0.52066763, -0.51645975, -0.51611537, -0.51792004, -0.52051112,\n",
       "         -0.52144684, -0.52147524],\n",
       "        [-0.52553459, -0.52677647, -0.53355884, -0.53898219, -0.54297471,\n",
       "         -0.54208838, -0.54447941]])}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L2.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T10:08:52.500442Z",
     "start_time": "2017-12-23T10:08:52.324146Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JUzz3yksKuXTZPLY0H+VP35nq1oiITE+yVyRlZrZueCEYDC8LFgfG3oVDQFXC8jLgSMJy\nOXARsCkYQL8C2GhmZw2eu/su4FSwLe5+JPjZSvyZlrVj/XJ3v8/d69y9LhqNJnOOKdEYi/DCoRN0\ndKv8rohkpmSD5A+Ar5rZvuBL/6vAH5hZKfA/x9lnG7DKzGrMrAi4Edg4/KG7d7h7xN2r3b0aeBrY\nENy1VWNmBQBmtgK4ANhvZqXB4DzB776O+MB8xqqPDZff1VPuIpKZkn0gcRtwsZnNI14t8UTCx98Z\nZ58BM7sFeIz47b/3B7VM7gCa3H3jWPsFGoHbzKwfGAL+0N3bgzooD5rZcNu/5e7/kcw5pKs1y+cz\npzCfLc3tXHfhealujojIlCUVJEGAfA64OljeDNzh7h0T7efujwCPjFr32XG2vTbh/TeBb46xTQtw\naTJtzhTFBflcXrOQLXt1RSIimSnZrq37iT8E+L7gdRL4WliNyjWNsUqaW7t4XeV3RSQDJRskte7+\nueCZkBZ3vx1YGWbDckl9raaVF5HMlWyQdJtZ4/CCmTUA3eE0KfesXlLBApXfFZEMlexzJB8l/izH\nPOLPhxxjnOc/ZOri5XcjI+V3g5sJREQyQlJXJO7+nLtfClwCXOzua9z9+XCbllvqY5W8drKHFpXf\nFZEMM9k08n8yznoA3P1LIbQpJyWW362Nlk2ytYhI+pjsiqR8kpfMkuULh8vvasBdRDLLZNPI336u\nGpLrzIzGWIRHX3yVwSEnP0/jJCKSGZK9a2uEmf0ijIZIfJzkZM8ALx6e8DlPEZG0MuUgYexZfWUW\njDxPslfdWyKSOaYTJA/PeisEgGh5MW8+r1zjJCKSUaYcJO7+l2E0ROLqayNs23+cnv7BVDdFRCQp\nyZba7TSzk6NeB83swWBGXpkljasq6RsYYvsBld8VkcyQ7JPtXyJelOpbxMdIbgTOA3YTn9Dx2jAa\nl4vW1lRSEJTfbQieLRERSWfJdm2td/d73b3T3U+6+33Ar7v7/wMWhNi+nFNWXMBlVfM1rbyIZIxk\ng2TIzN5nZnnB630Jn/m4e8m01Mci7FD5XRHJEMkGyU3AbwOtwOvB+w+a2RzglpDalrMag/K7T6v8\nrohkgGRL7bYA7x7n4ydmrzkCcFnVmfK771T5XRFJc8netXW+mf3UzF4Mli8xs0lvAzaz9Wa228ya\nzey2Cba7wczczOqC5bVm9lzwet7M3jvVY2ayooI81tYs1PMkIpIRku3a+grwaaAfwN1fIH7n1rjM\nLB+4B3gXsBr4gJmtHmO7cuBWYGvC6heBOne/DFgP3GtmBckeMxs0xiLsbTvFax0qvysi6S3ZIJnr\n7s+MWjcwyT5rgeagNG8f8ABw/Rjb3QncBYx8Y7r7aXcfPn4JZwb0kz1mxquPVQIqvysi6S/ZIGk3\ns1qCL3QzuwF4dZJ9lgIHE5YPBetGmNkaoMrdHxq9s5mtM7OdwA7gI0GwTHrMhP1vNrMmM2tqa2ub\npKnp5y3nVbCwtEjzbolI2ks2SD4G3Au82cwOAx8HPjLJPmNN7jhyq7CZ5QF3A58Ya2d33+ruFwKX\nA582s5LJjjlq//vcvc7d66LR6CRNTT95ecaVtZUj5XdFRNJVskFyGPga8AXi3Uk/Bn5nkn0OAVUJ\ny8uIPx0/rBy4CNhkZvuBK4CNwwPuw9x9F3Aq2HayY2aVxliE10/2srdN5XdFJH0lGyQ/IH77bz/x\nL+4u4l/uE9kGrDKzGjMrIj44v3H4Q3fvcPeIu1e7ezXwNLDB3ZuCfQoAzGwFcAGwf7JjZpuG2jPl\nd0VE0lWyc20tc/f1Uzmwuw+Y2S3AY0A+cL+77zSzO4Amd58oABqB28ysHxgC/tDd2wHGOuZU2pVJ\nllfOZdmCePnd36mvTnVzRETGlGyQPGlmF7v7jqkc3N0fAR4Zte6z42x7bcL7bwLfTPaY2awxFuHh\nHa8yMDhEQf50yseIiIQr2W+mRmB78CDgC2a2w8xeCLNhElcfi9DZM8CLR06muikiImNK9orkXaG2\nQsZVX3vmeZLLquanuDUiIm+U1BWJux8Y6xV24wQiZSq/KyLpTZ3uGaAxFqHpgMrvikh6UpBkgIZY\nhL6BIZr2q/yuiKQfBUkGWFuzMF5+V9OliEgaUpBkgNLiAtYsn69xEhFJSwqSDFFfG2HH4Q46Tqv8\nroikFwVJhmhcFcEdnlL5XRFJMwqSDHHpsvnMLcpX95aIpB0FSYYoKshjXc1CDbiLSNpRkGSQhliE\nlrZTvNrRneqmiIiMUJBkkPqRaeU1TiIi6UNBkkHefF45laVFGicRkbSiIMkgKr8rIulIQZJhGmMR\nWjt72dvWleqmiIgACpKM0xCLj5M88bK6t0QkPYQaJGa2PiiG1Wxmt02w3Q1m5mZWFyy/w8y2BwW0\ntpvZryRsuyk45nPBa1GY55BuqhbOZfnCuWzZqwF3EUkPyRa2mjIzywfuAd4BHAK2mdlGd39p1Hbl\nwK3A1oTV7cC73f2ImV1EvEb70oTPb3L3prDanu4aYpU89LzK74pIegjzW2gt0OzuLe7eBzwAXD/G\ndncCdwE9wyvc/Vl3PxIs7gRKzKw4xLZmlIZYhM7eAXYc7kh1U0REQg2SpcDBhOVDnH1VgZmtAarc\n/aEJjvNbwLPu3puw7mtBt9ZnzMxmrcUZ4sqVZ8rvioikWphBMtYX/Mg9q2aWB9wNfGLcA5hdCPwN\n8N8TVt/k7hcDVwWv3x5n35vNrMnMmtra2qbR/PRVWVbMW5ZU6MFEEUkLYQbJIaAqYXkZcCRhuRy4\nCNhkZvuBK4CNCQPuy4AHgQ+5+97hndz9cPCzE/gW8S60N3D3+9y9zt3rotHorJ1UumiMVbL9wHG6\n+1R+V0RSK8wg2QasMrMaMysCbgQ2Dn/o7h3uHnH3anevBp4GNrh7k5nNBx4GPu3uW4b3MbMCM4sE\n7wuB3wBeDPEc0lZ9LELf4BBNB46luikikuNCCxJ3HwBuIX7H1S7gO+6+08zuMLMNk+x+CxADPjPq\nNt9i4DEzewF4DjgMfCWsc0hna6sXUphv6t4SkZQL7fZfAHd/BHhk1LrPjrPttQnvPw98fpzDvm22\n2pfJSosLWFO1QAPuIpJyegghgzXEIrx4pIMTp/tS3RQRyWEKkgzWEKuMl9/VU+4ikkIKkgx2adV8\nSovyVTVRRFJKQZLBCvPzWLeykic14C4iKaQgyXD1tZW0tJ/iyAmV3xWR1FCQZLjGVcPld9W9JSKp\noSDJcBcsLidSpvK7IpI6CpIMZ2bU10bYsveoyu+KSEooSLJAQ6ySts5eXm5V+V0ROfcUJFmgvlbj\nJCKSOgqSLFC1cC4rKudq3i0RSQkFSZaor42wteUoA4NDqW6KiOQYBUmWaAzK776g8rsico4pSLLE\nlbVB+d2XNU4iIudWqNPIy7mzsLSIC99UwZa97fzRr65KdXNEZBb1DgzS0d3Pye5+Tpzup6P7zGt4\n+WR3Pye6z/6so7ufnbe/k8L8cK8ZFCRZpCEW4etb9tPdN8icovxUN0dEEvQPDnFy+Mu/+8yXf8eo\ncDhx+sz6+LZ99PRPPPZZXlzAvLmFzJsTf52/uIx5cwqpmFPI4JBTGPLXgYIki9TXVnLf4y1s23+M\nq8/Pvjr1Iqk2OOR09rzxy/+s1+n4l398eSC4iujjVN/ghMeeW5TP/ODLf96cQlZUzmV+QjjMm1t0\n5v2cQuYHP8tLCigI+YpjMgqSLLK2Jii/u7ddQSIyCXens3eAts7ekVdrZy/tXb0jVwVnAiEeHF29\nA0w0gURxQd5ZX/5L55eweknFmS//hM8qEpYrSgopKsjcIetQg8TM1gNfBvKBr7r7F8fZ7gbgu8Dl\n7t5kZu8AvggUAX3An7r7fwbbvg34OjCHeBnfP3bNDQLA3KIC1ixX+V3Jbb0Dg7R39SWEQ89ZYdHW\ndeZ978Abu4wK8uysL/xoWTGrFpWPfPknXg0MdycNX0mUhN2HlKZCCxIzywfuAd4BHAK2mdlGd39p\n1HblwK3A1oTV7cC73f2ImV0EPAYsDT77J+Bm4GniQbIeeDSs88g0jbEId/9kD8dP9bGgtCjVzRGZ\nFUNDzvHTfSMh0Hry7EBIDIiO7v4xj7GwtIhoWTHR8mKqq0uJlhePLC8qj/+Mlhczb04hZnaOzzCz\nhXlFshZodvcWADN7ALgeeGnUdncCdwGfHF7h7s8mfL4TKDGzYmAhUOHuTwXH/AbwHhQkIxpilXzp\nx/BUy1F+/eIlqW6OyIRODXctjQREz9kBEbxv7+pjcOiNHQ9zCvNZVFEcXDWUUV9bSbSsOL6uvJho\nWQnR8mIqy4pCv3Mpl4UZJEuBgwnLh4B1iRuY2Rqgyt0fMrNPMrbfAp51914zWxocJ/GYS8feLTdd\nsmw+ZcUFbGluV5BISvQPDnF0uGupqyd+9TCqS2n4/ekxBqDz84xIWdHIFcPqJRUsKi8ZuWJIvJIo\nLdYwbzoI809hrGvDkf9SmFkecDfwu+MewOxC4G+A65I55qh9bybeBcby5cuTanA2KMzPY13NQp7c\nq3m3JBxDQ86rJ3toaetiX/spWtpOsa/9FK91xK8mjp3qG3O/eXMKR0Lg0mXzz+pOSgyIBXOLyMtT\n11ImCTNIDgFVCcvLgCMJy+XARcCmoD/yPGCjmW0IBtyXAQ8CH3L3vQnHXDbBMUe4+33AfQB1dXU5\nNRhfH4vw01+2cvhEN0vnz0l1cyRDnTjdx94gJPa1d40Exr72U2cNUpcW5VMTLWVF5Vwur1kw0p2U\n+IqUFVFckJsD0bkgzCDZBqwysxrgMHAj8F+HP3T3DiAyvGxmm4BPBiEyH3gY+LS7b0nY51Uz6zSz\nK4gPzn8I+PsQzyEjNcbOTCv/vrqqSbaWXNbTP8iBo6dpaeuiJQiJ4SuN46fPDFoX5BnLF85lZbSU\nq1ZFqImUURMppTYaH7TW4HRuCy1I3H3AzG4hfsdVPnC/u+80szuAJnffOMHutwAx4DNm9plg3XXu\n3gp8lDO3/z6KBtrf4PzFZUTKihUkAsQfojtyojseFMPdUUGX1JGO7rOei1hcUUxNpJR3XbyElZFS\naiKlrIyWsWzBHA1Wy7hCHaly90eI36KbuO6z42x7bcL7zwOfH2e7JuJdYjIOM6MhVsmW5nj5Xf1v\nMfu5O8dP97OvvetMd1TbKVrau9h/9DR9CV1RZcUFrIyWUle9gJWRKmqipayMlFIdKaVMg9cyDfpb\nk6UaaiP84Lkj7Hm9iwvOK091c2SWdPcNsv/o8AB3YnfUqbOenyjMj3dF1UTKePsFi6hJuLqIlBXp\nPxcyqxQkWaph1ZlxEgVJZhkccg4f76Zl1AB3S1sXRzp6ztp2ybwSaiKl/MYlS1gZLRvpjlq2YE7K\n51+S3KEgyVJL58+hunIuT+5t58ONNalujozhVO8AL7168sxAd1t87OKVo6fpS6h0WV5SwMpoGetW\nVgZXFaUjVxhzi/RPWFJPfwuzWEMs3r01MDik/52mAXdn9+udbNrdxubdbTQdOEb/YHykuyg/jxWV\nc1kZKeVX37KI2kgZNUFgVJaqK0rSm4IkizXEIvzr1ld4/lAHb1uxINXNyUkd3f1saW5n0+5WNu9p\n4/WTvQC8+bxyfr9xJetqFlIbLWPpgjnk6yE8yVAKkix25cpKzOLjJAqSc2NoyNl55ORIcDx78ASD\nQ05FSQFXrYpyzQVRrjk/yuKKklQ3VWTWKEiy2ILh8rvN7dyq8ruhOXaqj5+/3Mam3W08vqeNo8EU\nIZcsm8fHrq3lmguiXLpsvroXJWspSLJcQ22E+7fs43TfgAZmZ8ngkPPcwRNs3tPG5t2tvHC4A/f4\nNOVXr4pwzQVRrloVJVJWnOqmipwT+mbJcg2xCPc+3sK2/ce5RlUTp631ZA+b97SxaU8bT7zcTkd3\nP3kGa5Yv4H/82vlcc36Ui5fO02SDkpMUJFnu8uqFFOXn8WRzu4JkCvoHh9h+4Hg8PHa3sevVkwAs\nKi/mutWL41cdsSjz5hamuKUiqacgyXJzivJ564r5PKHyu5M6fKKbzbvb2LynlS3NR+nqHaAgz6ir\nXsCn1r+Za86P8pYl5boVV2QUBUkOaKiN8KWf7OHYqT4WqvzuiJ7+QbbtPxaERxsvt3YB8Yc5N1z2\nJq45P0p9bSXlJbrqEJmIgiQH1Mci/O8f7+G9/7iFqgVzWVRezKKKEhZXFLOo/MzPRRXFlBRmd82I\nA0dPxR8I3NPGU3uP0t0/SFGrU9IwAAAImElEQVR+HutWLuT9l1dx7QVRaqNluuoQmQIFSQ5YUzWf\nP/qVGHte76S1s5et+07R2tkz8lR1onlzCllUXsziiniwJAbN4or4+mh55gROd98gT7W0j1x17D96\nGoDqyrm8//Iqrjk/yrqVC3VHm8gM6F9PDsjLMz5x3QVnrRsack509/P6yR5aO3vjPxPfd/aytWXi\nwEm8kkkMmuEgSkXguDt727pGrjq27jtG38AQcwrzubK2kg831nD1qijVkdJz2i6RbKYgyVF5ecbC\n0iIWlhbxliXjb5cYOMMBkxg4r5/sZd8UAicxaIbXzzRwOnv62dJ8lM174g8EHj7RDcCqRWV86IoV\nXHvBIuqqF2TMVZRIplGQyITODpyKcbcbGnKOn+4764qmNQia1s74z5a9XbR19Y4ZOPPnFp51JbO4\nooTFo8ZyhgPH3dn1aieb9rSyeXcb2w8cZ2DIKSsuoCFWycfeHuOaC6KqVy9yjihIZFbk5RmVZcVU\nlhUnFTjDAdOaEDTDAbS3tYvWzl4GhsYOnDwzjgXTkKxeUsF/u3ol15wf5W0rFqgcrEgKhBokZrYe\n+DLxmu1fdfcvjrPdDcB3gcvdvcnMKoHvAZcDX3f3WxK23QQsAbqDVcO13CUDJAbOaqYWOMNB090/\nyLqahVxzfpRFmvxQJOVCCxIzywfuAd4BHAK2mdlGd39p1HblwK3A1oTVPcBniNdmH6s++01B7XbJ\nUskGjoikXpj9AGuBZndvcfc+4AHg+jG2uxO4i3h4AODup9z9icR1IiKSnsIMkqXAwYTlQ8G6EWa2\nBqhy94emeOyvmdlzZvYZG+fJMTO72cyazKypra1tiocXEZFkhRkkY33Bj4yemlkecDfwiSke9yZ3\nvxi4Knj99lgbuft97l7n7nXRqCYrFBEJS5hBcgioSlheBhxJWC4nPv6xycz2A1cAG82sbqKDuvvh\n4Gcn8C3iXWgiIpIiYQbJNmCVmdWYWRFwI7Bx+EN373D3iLtXu3s18DSwYaJBdDMrMLNI8L4Q+A3g\nxRDPQUREJhHaXVvuPmBmtwCPEb/9935332lmdwBN7r5xov2Dq5QKoMjM3gNcBxwAHgtCJB/4CfCV\nsM5BREQmZ+5vfOgr29TV1XlTk+4WFhGZCjPb7u4TDjdAuF1bIiKSA3LiisTM2oh3i01HBMiW8oLZ\nci7Zch6gc0lX2XIuMz2PFe4+6W2vOREkM2FmTclc2mWCbDmXbDkP0Lmkq2w5l3N1HuraEhGRGVGQ\niIjIjChIJndfqhswi7LlXLLlPEDnkq6y5VzOyXlojERERGZEVyQiIjIjCpIkmNmdZvZCMOPwj8zs\nTalu03SY2d+a2S+Dc3nQzOanuk3TZWb/xcx2mtnQZPOzpSszW29mu82s2cxuS3V7psvM7jezVjPL\n6OmKzKzKzH5mZruCv1t/nOo2TZeZlZjZM2b2fHAut4f6+9S1NTkzq3D3k8H7W4HV7v6RFDdryszs\nOuA/g+lr/gbA3T+V4mZNi5m9BRgC7gU+mWmFzoLCb3tIKPwGfGB04bdMYGZXA13AN9x9rEJ0GcHM\nlgBL3P0XQcG97cB7MvTPxIBSd+8KppR6Avhjd386jN+nK5IkDIdIoJSE6fAzibv/yN0HgsWnic/I\nnJHcfZe77051O2Yg2cJvac/dHweOpbodM+Xur7r7L4L3ncAuRtVQyhQe1xUsFgav0L63FCRJMrMv\nmNlB4Cbgs6luzyz4MPBoqhuRwyYt/CapY2bVwBrOLgGeUcws38yeA1qBH7t7aOeiIAmY2U/M7MUx\nXtcDuPtfuHsV8K/ALalt7fgmO49gm78ABoifS9pK5lwy2ISF3yR1zKwM+D7w8VG9ERnF3Qfd/TLi\nPQ9rzSy0bsfQppHPNO7+a0lu+i3gYeBzITZn2iY7DzP7HeJ1XH7V03yAbAp/JplossJvkgLBeML3\ngX91939LdXtmg7ufMLNNwHpCqt+kK5IkmNmqhMUNwC9T1ZaZMLP1wKeIFxA7ner25LgJC7/JuRcM\nUP8zsMvdv5Tq9syEmUWH78o0sznArxHi95bu2kqCmX0fuID4XUIHgI8Ml/zNJGbWDBQDR4NVT2fi\n3WcAZvZe4O+BKHACeM7d35naVk2Nmf068HecKfz2hRQ3aVrM7NvAtcRnmn0d+Jy7/3NKGzUNZtYI\n/BzYQfzfOsCfu/sjqWvV9JjZJcC/EP+7lQd8x93vCO33KUhERGQm1LUlIiIzoiAREZEZUZCIiMiM\nKEhERGRGFCQiIjIjChKRWWBmXZNvNeH+3zOzlcH7MjO718z2BjO3Pm5m68ysKHivB4klrShIRFLM\nzC4E8t29JVj1VeKTIK5y9wuB3wUiweSOPwXen5KGioxDQSIyiyzub4M5wXaY2fuD9Xlm9o/BFcZD\nZvaImd0Q7HYT8INgu1pgHfCX7j4EEMwQ/HCw7b8H24ukDV0ii8yu3wQuAy4l/qT3NjN7HGgAqoGL\ngUXEpyi/P9inAfh28P5C4k/pD45z/BeBy0Npucg06YpEZHY1At8OZl59HdhM/Iu/Efiuuw+5+2vA\nzxL2WQK0JXPwIGD6gsJLImlBQSIyu8aaHn6i9QDdQEnwfidwqZlN9G+zGOiZRttEQqEgEZldjwPv\nD4oKRYGrgWeIlzr9rWCsZDHxSQ6H7QJiAO6+F2gCbg9mo8XMVg3XYDGzSqDN3fvP1QmJTEZBIjK7\nHgReAJ4H/hP4s6Ar6/vEa5C8SLzO/FagI9jnYc4Olj8AzgOazWwH8BXO1Cp5O5Bxs9FKdtPsvyLn\niJmVuXtXcFXxDNDg7q8F9SJ+FiyPN8g+fIx/Az6d4fXqJcvori2Rc+ehoNhQEXBncKWCu3eb2eeI\n12x/ZbydgwJY/64QkXSjKxIREZkRjZGIiMiMKEhERGRGFCQiIjIjChIREZkRBYmIiMyIgkRERGbk\n/wMjtVL+oj4LPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0e7e6da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_Cs = len(Cs)\n",
    "n_classes = 3\n",
    "scores =  np.zeros((n_classes,n_Cs))\n",
    "\n",
    "for j in range(n_classes):\n",
    "        scores[j][:] = np.mean(lrcv_L2.scores_[j],axis = 0)\n",
    "    \n",
    "mse_meanan = -np.mean(scores, axis = 0)\n",
    "plt.plot(np.log10(Cs), mse_mean.reshape(n_Cs,1)) \n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('neg-logloss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T10:01:21.619647Z",
     "start_time": "2017-12-23T10:01:21.600305Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1.27917477e-01,   8.88125998e-02,  -3.74129234e-02,\n",
       "         -9.01082995e-02,  -4.85936057e-01,  -7.95716556e-01,\n",
       "         -4.04921192e-01,  -1.63533963e-01,   2.81752502e-02,\n",
       "          0.00000000e+00,   1.04994785e-02,  -9.57901973e-02,\n",
       "          4.82809250e-02,  -2.29659111e-02,   2.61068316e-01,\n",
       "         -1.05541948e-01,   2.92169145e-02,   1.32160690e-01,\n",
       "          1.51851061e-02,  -1.18072362e-01,  -1.19022742e-01,\n",
       "         -2.96813223e-02,   9.02957609e-03,  -3.68444368e-02,\n",
       "         -6.20201762e-02,  -2.11422267e-03,  -6.24124019e-02,\n",
       "          6.02981956e-03,  -5.88249521e-02,  -2.99796260e-02,\n",
       "          6.93760279e-02,  -8.80741378e-02,  -1.12029748e-02,\n",
       "         -1.38998402e-02,   2.52666190e-02,  -7.45390256e-02,\n",
       "         -3.31911280e-02,  -7.18343623e-02,  -5.72818480e-03,\n",
       "          2.66158301e-02,   4.73861021e-02,  -7.25788872e-02,\n",
       "          3.81136785e-02,  -1.76853676e-02,  -1.75806349e-02,\n",
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       "       [  1.85190049e-01,  -1.37484978e-01,   1.14676748e-01,\n",
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       "          2.78759127e-02,  -1.21669677e-02,  -5.23034863e-02,\n",
       "          1.92254902e-02,   2.37294700e-02,   2.47411698e-02,\n",
       "          4.55694257e-02,   4.83166175e-02,  -1.34251760e-02,\n",
       "         -3.67179881e-02,   4.23405250e-02,   9.07473136e-03,\n",
       "         -8.09721736e-04,  -3.04124480e-02,   2.7599"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<b>limit_output extension: Maximum message size of 10000 exceeded with 13899 characters</b>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrcv_L2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-24T01:46:36.610202Z",
     "start_time": "2017-12-24T01:46:36.595317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.70620155038759691"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 当 C 的值为 0.1 lossloss 最小 ，此时的评分最高\n",
    "lrcv_L2.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### liblinear优化求解器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T11:21:13.932002Z",
     "start_time": "2017-12-23T10:52:53.613102Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "           class_weight=None, cv=5, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1.0, max_iter=100, multi_class='ovr',\n",
       "           n_jobs=1, penalty='l2', random_state=None, refit=True,\n",
       "           scoring='neg_log_loss', solver='liblinear', tol=0.0001,\n",
       "           verbose=0)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L2 = LogisticRegressionCV(Cs=Cs, cv = 5, scoring='neg_log_loss', penalty='l2', solver='liblinear', multi_class='ovr')\n",
    "lrcv_L2.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T11:26:42.731779Z",
     "start_time": "2017-12-23T11:26:42.722622Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: array([[-0.27300176, -0.23465697, -0.23602398, -0.24229379, -0.24965674,\n",
       "         -0.25458157, -0.25663819],\n",
       "        [-0.27362427, -0.23581488, -0.23779131, -0.24557201, -0.25255345,\n",
       "         -0.25714995, -0.26204895],\n",
       "        [-0.27124803, -0.23115349, -0.23227894, -0.23923395, -0.24607209,\n",
       "         -0.25699362, -0.26591202],\n",
       "        [-0.27200558, -0.23393525, -0.23862603, -0.24965272, -0.26248233,\n",
       "         -0.26846506, -0.27144601],\n",
       "        [-0.2733849 , -0.23662905, -0.24081863, -0.24918253, -0.25705655,\n",
       "         -0.26777258, -0.27198202]]),\n",
       " 1: array([[-0.50409292, -0.49808961, -0.50182726, -0.50696616, -0.51197609,\n",
       "         -0.51523371, -0.51663962],\n",
       "        [-0.5017571 , -0.49708903, -0.50209527, -0.50660902, -0.51049867,\n",
       "         -0.51250422, -0.51598897],\n",
       "        [-0.50671662, -0.50213349, -0.50501395, -0.51079219, -0.51652515,\n",
       "         -0.51924272, -0.52195628],\n",
       "        [-0.50515651, -0.49929167, -0.50177366, -0.50494158, -0.50767002,\n",
       "         -0.50949628, -0.51135489],\n",
       "        [-0.5055621 , -0.50177787, -0.508471  , -0.51294771, -0.51650718,\n",
       "         -0.51756377, -0.5177107 ]]),\n",
       " 2: array([[-0.52021204, -0.51076824, -0.51129854, -0.51401483, -0.51727515,\n",
       "         -0.52012465, -0.52186033],\n",
       "        [-0.52161926, -0.51338965, -0.51310079, -0.51471526, -0.51607333,\n",
       "         -0.51694777, -0.51786164],\n",
       "        [-0.53244513, -0.52894529, -0.53202755, -0.5381993 , -0.54265373,\n",
       "         -0.54446032, -0.54501231],\n",
       "        [-0.52449494, -0.5162902 , -0.51596047, -0.51762452, -0.51972399,\n",
       "         -0.5215092 , -0.52138418],\n",
       "        [-0.52974144, -0.52649112, -0.53329126, -0.53833565, -0.54147965,\n",
       "         -0.54349078, -0.54544124]])}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L2.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T11:26:46.494187Z",
     "start_time": "2017-12-23T11:26:46.292553Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JUzz3yksKuXTZPLY0H+VP35nq1oiITE+yVyRlZrZueCEYDC8LFgfG3oVDQFXC8jLgSMJy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\n/wMjtVL+oj4LPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a122b5470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_Cs = len(Cs)\n",
    "n_classes = 3\n",
    "scores =  np.zeros((n_classes,n_Cs))\n",
    "\n",
    "for j in range(n_classes):\n",
    "        scores[j][:] = np.mean(lrcv_L2.scores_[j],axis = 0)\n",
    "    \n",
    "mse_meanan = -np.mean(scores, axis = 0)\n",
    "plt.plot(np.log10(Cs), mse_mean.reshape(n_Cs,1)) \n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('neg-logloss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T10:48:25.160054Z",
     "start_time": "2017-12-23T10:48:25.140184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1.35386931e-01,   8.37801508e-02,  -6.94052434e-02,\n",
       "         -1.11942999e-01,  -3.33039447e-01,  -7.59782282e-01,\n",
       "         -3.22415400e-01,  -1.64581388e-01,   1.93374020e-02,\n",
       "          0.00000000e+00,   9.77154151e-03,  -8.37390530e-02,\n",
       "          4.33759511e-02,  -2.14550780e-02,   2.36151867e-01,\n",
       "         -9.92229530e-02,   2.67092966e-02,   1.23732504e-01,\n",
       "          1.41046771e-02,  -1.00092394e-01,  -1.08390720e-01,\n",
       "         -2.78041545e-02,   7.72333717e-03,  -3.63976903e-02,\n",
       "         -5.27938874e-02,  -2.43051270e-03,  -3.91634023e-02,\n",
       "          5.59056174e-03,  -5.14446257e-02,  -4.37403223e-02,\n",
       "          7.02564433e-02,  -7.67552479e-02,  -3.94757082e-03,\n",
       "         -1.22155503e-02,   2.25917240e-02,  -8.02979882e-02,\n",
       "         -3.31908387e-02,  -8.62564063e-02,  -7.67985811e-03,\n",
       "          2.45131244e-02,   4.10702178e-02,  -6.22884298e-02,\n",
       "          3.98457041e-02,  -1.55446077e-02,  -2.14132141e-02,\n",
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       "          1.73258454e-02,   2.74478414e-02],\n",
       "       [  2.25723374e-01,  -1.33279016e-01,   1.60703788e-01,\n",
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       "         -3.54896665e-02,   3.21880142e-02,  -2.77553609e-01,\n",
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       "          2.39279184e-02,   1.41537608e-01,  -7.35793110e-03,\n",
       "          2.76808146e-02,  -1.25819156e-02,  -5.19962323e-02,\n",
       "          1.87189362e-02,   2.36805293e-02,   2.40616334e-02,\n",
       "          4.55909456e-02,   4.74640391e-02,  -2.32056098e-02,\n",
       "         -3.84327381e-02,   4.25066377e-02,   1.01947130e-02,\n",
       "          2.71404405e-03,  -3.00219115e-02,   6.1843"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<b>limit_output extension: Maximum message size of 10000 exceeded with 13899 characters</b>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrcv_L2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-24T01:46:05.037375Z",
     "start_time": "2017-12-24T01:46:05.020338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.70620155038759691"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 当 C 的值为 0.1 lossloss 最小 ，此时的评分最高\n",
    "lrcv_L2.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RBF核SVM正则参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T11:26:53.195094Z",
     "start_time": "2017-12-23T11:26:53.183936Z"
    },
    "cell_style": "center",
    "hide_input": false,
    "lang": "en"
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "def fit_grid_point_RBF(C , gamma , X_train , y_train , X_test , y_test):\n",
    "    svc = SVC(C = C, kernel = 'rbf', gamma = gamma)\n",
    "    svc = svc.fit(X_train,y_train)\n",
    "    \n",
    "    accuracy = svc.score(X_test,y_test)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T20:20:45.880539Z",
     "start_time": "2017-12-23T11:26:59.644798Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.6980620155038759\n",
      "accuracy: 0.6980620155038759\n",
      "accuracy: 0.6980620155038759\n",
      "accuracy: 0.6980620155038759\n",
      "accuracy: 0.6980620155038759\n",
      "accuracy: 0.6989664082687338\n",
      "accuracy: 0.6980620155038759\n",
      "accuracy: 0.6980620155038759\n",
      "accuracy: 0.6980620155038759\n",
      "accuracy: 0.6980620155038759\n",
      "accuracy: 0.7002583979328165\n",
      "accuracy: 0.7112403100775194\n",
      "accuracy: 0.6974160206718346\n",
      "accuracy: 0.6983204134366925\n",
      "accuracy: 0.6976744186046512\n",
      "accuracy: 0.7149870801033592\n",
      "accuracy: 0.6866925064599483\n",
      "accuracy: 0.6922480620155039\n",
      "accuracy: 0.6981912144702842\n",
      "accuracy: 0.6978036175710595\n",
      "accuracy: 0.6935400516795865\n",
      "accuracy: 0.6733850129198966\n",
      "accuracy: 0.6922480620155039\n",
      "accuracy: 0.6981912144702842\n",
      "accuracy: 0.6978036175710595\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-2, 2, 5) \n",
    "gamma_s = np.logspace(-2, 2, 5)  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_test, y_test)\n",
    "        accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-24T00:36:55.145032Z",
     "start_time": "2017-12-24T00:36:54.862151Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zu2nX8plu79Rwbj9651yo0ArCml6f73SidcbTxrquxenIUnuufclIvawj99dR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\nMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0f25a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    plt.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuracy' )\n",
    "plt.savefig('RBF_SVM_RentalListingInquiries.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-24T02:09:46.221691Z",
     "start_time": "2017-12-24T02:09:46.212997Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score is  0.714987080103\n"
     ]
    }
   ],
   "source": [
    "# 如上图可知 gamma 为 2 ，C 为 10 ，评分最高\n",
    "print('best score is ',accuracy_s1.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-24T01:50:56.297624Z",
     "start_time": "2017-12-24T01:50:56.293782Z"
    }
   },
   "source": [
    "## 模型选择\n",
    "SVM 的评分最高，选用 SVM　RBF 内核 作为模型 预测测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-24T02:52:17.990520Z",
     "start_time": "2017-12-24T02:18:34.514115Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svc = SVC(C = 10, kernel = 'rbf', gamma = 2)\n",
    "svc = svc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对测试集进行测试，生成提交文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-24T02:52:25.490263Z",
     "start_time": "2017-12-24T02:52:25.399337Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "listing_id = target['listing_id']\n",
    "X_test_pred = target.drop('listing_id',axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-24T03:02:16.192711Z",
     "start_time": "2017-12-24T02:52:31.579521Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 2, 2, 2])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred = svc.predict(X_test_pred)\n",
    "y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-24T03:10:19.156996Z",
     "start_time": "2017-12-24T03:10:18.949664Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#生成提交测试结果\n",
    "y = pd.Series(data = y_test_pred, name = 'interest_level')\n",
    "df = pd.concat([listing_id, y], axis = 1)\n",
    "df.to_csv('submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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